Spatial Optimization Methods for Malaria Risk Mapping in Sub‐Saharan African Cities Using Demographic and Health Surveys

Abstract

Abstract Vector‐borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub‐Saharan Africa (SSA). In this context, intra‐urban malaria risk maps act as a key decision‐making tool for targeting malaria control interventions, especially in resource‐limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra‐urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra‐urban malaria risk in SSA cities—Dakar, Dar es Salaam, Kampala and Ouagadougou—and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%–40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra‐urban scale.

Keywords

• urban malaria
• sub‐Saharan Africa
• DHS
• remote sensing
• random forest